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所在平台: Udemy |
课程主页: https://www.udemy.com/course/regression-with-minitab/
课程评论：没有评论
**课程名称：** Multiple Regression with Minitab **课程概述：** 本课程将深入讲解最常用的分析技术之一：回归分析。课程以六西格玛黑带大师级别为切入点，聚焦多元回归分析。教学过程将使用 Minitab 19 进行实操，重点在于概念的阐释以及分析结果的解读。 **课程内容：** 课程从基础开始，包括散点图的绘制和简单回归（仅一个预测变量）的学习。在 Minitab 19 中的分析过程和输出结果将进行详细解释。课程采用“学习时数与考试成绩”这一简单示例来帮助理解概念，随着课程的推进，示例将变得更加复杂。最终，课程将以分析和建模保险费用与多种因素的关系作为项目实践。 **核心知识点：** * 简单线性回归 * 多元回归 * 非线性回归（多项式） * 偏差-方差权衡 * 使用最佳子集和逐步选择方法进行特征选择 * 异常值识别 * 训练集和测试集：验证集方法、留一法交叉验证和 K 折交叉验证 * 响应变量预测 * **项目实践：** 医疗保险费用分析 **课程特色：** * **实操性强：** 完全基于 Minitab 19 进行分析演示。 * **深入浅出：** 从基础概念讲起，逐步深入到复杂的模型构建。 * **结果导向：** 强调对分析结果的理解和应用。 * **涵盖高级主题：** 包含特征选择、模型验证等进阶内容。 * **理论与实践结合：** 通过具体的项目实践加深对知识的掌握。 **适合人群：** 对数据分析、统计建模感兴趣，希望掌握多元回归分析技术，并能够熟练使用 Minitab 19 进行实操的学习者。 **（注意：课程大纲未提供，以上内容为基于课程名称和概述的总结。）**
In this course, I will teach you one of the most commonly used analytical techniques: Regression Analysis.This course covers the top of multiple regression analysis at the Six Sigma Master Black Belt level.I will use Minitab 19 to perform the analysis. The focus of my teaching will be on explaining the concepts and on analyzing and interpreting the results of the analysis.The course starts from the basics, covering the scatter plot and learning the simple regression with just one predictor. The analysis is conducted in Minitab 19, and the results of the output are explained in detail. To understand the concept, a simple example of hours of studies and marks obtained in the exam is taken. As you move through the course the example becomes more complex. In the end, we analyzed and modelled the insurance cost based on various factors.This course also covers hypothesis testing, understanding the p-value to interpret the result.Later, additional predictors are added to the regression model. The performance of the model is understood by interpreting the value of R-squared and adjusted R-squared.The following concepts are covered in this course:Simple Linear RegressionMultiple RegressionNonlinear Regression (Polynomial)Bias Variance Trade-offSelecting features using Best Subsets and Stepwise selection approachesIdentifying OutliersTraining and Test Data - Validation set approach, Leave one out cross-validation and K-Fold Validation.Predicting ResponseProject Work - Medical Insurance Charges
